EESS talk - Student presentation on "Advancing cloud representation in large-scale models: Introducing RaFSIP framework for ice multiplication in mixed-phase clouds"


Event details

Date 14.05.2024
Hour 12:3513:05
Speaker Paraskevi Georgakaki, LAPI
Location Online
Category Conferences - Seminars
Event Language English
Simulating clouds and their impact on climate has been a long-standing challenge for global climate models (GCMs), largely due to limitations in representing small-scale microphysical processes, typically addressed through empirical parameterizations. Mechanisms related to ice formation have been notably overlooked compared to liquid-phase processes. As a result, many weather prediction models and GCMs lack descriptions of critical ice multiplication processes capable of efficiently amplifying ice crystal concentrations at relatively warm subzero temperatures. In this talk, we introduce a novel framework called RaFSIP, specifically designed to represent the impact of ice multiplication in polar stratiform mixed-phase clouds – recognized as the most radiatively important cloud type. RaFSIP leverages machine learning techniques applied to regional climate simulations, streamlining its integration into large-scale models. We incorporate RaFSIP into three European GCMs (ECHAM-HAM, NorESM, EC-Earth) within the FOR-ICE intercomparison project to assess its influence on predicted radiation patterns and precipitation processes.

Short Biography:
I hold a BSc in Physics from the University of Crete and an MSc in Environmental Physics and Meteorology from the University of Athens. I am currently working as a postdoctoral researcher at the Laboratory of Atmospheric Processes and their Impacts (LAPI), where I completed my PhD in December 2023 under the supervision of Prof. Athanasios Nenes. My PhD research aimed to address critical knowledge gaps in cloud physics related to the production and multiplication of ice particles in the atmosphere, with a focus on how these processes are represented in large-scale atmospheric models. Refining the representation of such small-scale cloud processes in global climate models is important for improving the accuracy of future climate projections.

Practical information

  • General public
  • Free
  • This event is internal


  • EESS - IIE


  • Prof. Athanasios Nenes LAPI


Cloud microphysics numerical weather prediction mixed-phase clouds secondary ice production machine learning parameterization model simplification